Media Mix Modeling (MMM) And Estimating KPIs in the Face of SKAN Limitations

Performance data has proven to be a valuable resource for decision-making, but obtaining insights has become more complex.

The limitations of SKAdNetwork, such as the max 72-hour window and privacy threshold, pose challenges in capturing a comprehensive view of user conversions (SKAN 4.0 will improve the limitations when channels adopt it fully).

Given the limitations of SKAN, marketers are required to make estimations for key iOS KPIs and make educated decisions in their performance campaigns.

What is Media Mix Modeling (MMM)?

SKAdNetwork’s limitations may result in data gaps and incomplete insights, especially for specific KPIs (Cost Per Event, LTV, e.g.) or segments (retention, user quality). Media Mix Modeling can bridge these gaps by leveraging additional data sources and statistical modeling techniques.

MMM allows marketers to attribute post-install events or KPIs to marketing channels beyond what SKAdNetwork can track. By analyzing historical data and integrating consenting user analysis, estimations can provide insights into specific channels’ effectiveness, even without precise SKAdNetwork measurements.

By incorporating estimations for untracked or underreported channels, MMM can provide a more accurate assessment of the overall performance and help marketers evaluate the effectiveness of their media mix decisions.

How Does MMM Work?

Media Mix Modeling can be calibrated and validated using available SKAdNetwork data as a reference point. By comparing the estimations derived from MMM with the actual SKAdNetwork measurements, marketers can fine-tune and validate the model’s accuracy, ensuring that the estimations align closely with the observed data.

Estimates will vary greatly depending on each case, and calculations should be tailored to specific scenarios or in-app events. That’s where expertise meets data, and all variables should be considered. Estimations should factor in lengths of free trials, cohort analysis with different timeframes, audience segmentation, etc.

Advantages and Disadvantages of Standard Media Mix Modeling

Media Mix Modeling (MMM) offers several advantages and disadvantages for marketers in app marketing. Understanding the pros and cons listed below can help marketers make informed decisions about implementing MMM in their strategies.

Advantages of MMM

• Data-driven insights: it provides valuable insights into the effectiveness of different media channels by analyzing historical data. Marketers can make data-driven decisions about budget allocation and media planning, optimizing their strategies for better results.
• ROI optimization: media mix modeling helps marketers identify the media channels that generate the highest return on investment (ROI). Marketers can maximize their marketing efforts and achieve better ROI by allocating resources to the most effective channels.
• Synergy analysis: it enables marketers to evaluate the synergistic effects of combining different media channels. Understanding how channels work together allows for more effective integrated marketing campaigns, leveraging the strengths of each channel to amplify overall impact.

Disadvantages of MMM

• Complexity and expertise: implementing MMM requires statistical modeling expertise and a deep understanding of data analysis. It can be a complex process that requires skilled professionals and adequate resources.
• Time and resources: MMM requires substantial data collection and analysis. Gathering the necessary data, processing it, and running the models can be time-consuming and resource-intensive.
• Limited granularity: media mix modeling operates on aggregated data, which can limit the granularity of insights. Individual-level data may not be available or feasible to analyze, potentially missing out on specific nuances or individual channel effects.
• Lag time: it relies on historical data, which means there may be a lag between the actual marketing efforts and the availability of insights. Real-time adjustments may not be possible, impacting the agility of marketing campaigns.

The Four Standard Stages of an MMM Process

The four steps of an effective, standard MMM procedure are as follows:

1. Collection of data: here, you must concentrate on gathering first-party data in order to provide a more precise picture of user reactions and behavior in regard to your marketing strategy. Collect data on prior marketing, non-marketing, and outside factors. Example: user engagement, target audience demographics, and ad spend.
2. Modeling: MMM works better with online channels. Traditional techniques, such as print and broadcasts, are more difficult to track. As an app marketer, you may use multi-linear regression to calculate ROI and provide reliable, credible decision-making insights. Pick a dependent variable like revenue or app downloads to build an MMM model. Determine the independent variables.
3. Insights and data analysis: you’ll utilize the model from phase 2 to identify and analyze insights about your marketing initiatives in this stage. Assess each channel’s impact on the business outcomes and reliable metrics you established. Based on income or user interaction, you can rank your marketing initiatives. Media effectiveness, efficiency, and ROI can be measured for each campaign.
4. Optimization: here, you adjust your marketing mix for future campaigns based on phase three results. Try replicating multiple marketing situations, targeting different groups, or adjusting ad spend levels to determine the best combination of techniques for achieving your revenue targets faster.

How We Estimate Post-event Results in SKAN Campaigns

Below is a general scenario that we use. Specific cases may require specific adjustments to the example below. The idea here is to incorporate the “CAMPAIGN A” consenting user conversion rate (CR%) into “CAMPAIGN A” SKAN numbers.

In cases where the privacy threshold is not passed:

In cases where the privacy threshold is passed, but we want a CPA for a longer cohort period (e.g., 14 days):

Note: In cases where there is insufficient consenting user data, we consider organic users as well.

Conclusion

Marketers should approach Media Mix Modeling (MMM) and estimations as iterative processes, constantly updating and refining their models as new data becomes available. This approach allows them to adapt to changing market dynamics and consumer behavior, ensuring their media strategies remain optimized over time.

With the inclusion of SKAN in a marketer’s reality, it has become essential to acknowledge its limitations and the need for data. In order to overcome these limitations, marketers should employ educated estimations to supplement the available data.

Need help measuring the true impact of a campaign? Our team of experts will help you measure, manage and analyze marketing performance data to understand the effectiveness and improve ROI.